Clinical concept annotation with contextual word embedding in active transfer learning environment.

Objective: The study aims to present an active learning approach that automatically extracts clinical concepts from unstructured data and classifies them into explicit categories such as Problem, Treatment, and Test while preserving high precision and recall and demonstrating the approach through experiments using i2b2 public datasets. Methods: Initially labeled data are acquired from a lexical-based approach in sufficient amounts to perform an active learning process. A contextual word embedding similarity approach is adopted using BERT base variant models such as ClinicalBERT, DistilBERT, and SCIBERT to automatically classify the unlabeled clinical concept into explicit categories. Additionally, deep learning and large language model (LLM) are trained on acquiring label data through active learning. Results: Using i2b2 datasets (426 clinical notes), the lexical-based method achieved precision, recall, and F1-scores of 76%, 70%, and 73%. SCIBERT excelled in active transfer learning, yielding precision of 70.84%, recall of 77.40%, F1-score of 73.97%, and accuracy of 69.30%, surpassing counterpart models. Among deep learning models, convolutional neural networks (CNNs) trained with embeddings (BERTBase, DistilBERT, SCIBERT, ClinicalBERT) achieved training accuracies of 92-95% and testing accuracies of 89-93%. These results were higher compared to other deep learning models. Additionally, we individually evaluated these LLMs; among them, ClinicalBERT achieved the highest performance, with a training accuracy of 98.4% and a testing accuracy of 96%, outperforming the others. Conclusions: The proposed methodology enhances clinical concept extraction by integrating active learning and models like SCIBERT and CNN. It improves annotation efficiency while maintaining high accuracy, showcasing potential for clinical applications.